Senior Data Engineer

Senior Data Engineer

Experience: 6–10 Years

Location: Bangalore / Noida

About Pine Labs

Pine Labs is redefining the way the world experiences payments. Since 1998, we've grown from a single-point card acceptance solution into a global commerce platform trusted by 350,000+ merchants. From POS systems and digital payments to BNPL, EMI, loyalty, and gift card solutions, we empower businesses to dream bigger, serve better, and grow faster. With a presence across India, Southeast Asia, and the Middle East, our mission is to make every transaction smart, seamless, and full of possibility—helping merchants connect deeply with customers and shape the future of commerce.

With our continued leadership in all facets of money movement, Pine labs processes petabytes of transactional and user-behavioral signals on a monthly basis. This data is not only the backbone of our core processing engines but also provides very high value-add to our merchants and users alike. Data product group is chartered with creating a robust unified data platform as well as building high quality data application suite in a legal and compliant way. As part of this endeavour , we are right now in an exciting 0-1 journey of building next generation AdTech and Marketing stacks that help our customers achieve more.

About the Role

We are looking for a hands-on Senior Data Engineer to build and scale our next-generation data platform powering analytics, reporting, AI workloads, and data-driven products. You will work at the intersection of data engineering, platform engineering, and business intelligence, enabling high-quality, reliable, and compliant data systems that support both internal stakeholders and customer-facing applications.

You will own the end-to-end lifecycle of data pipelines, data models, and platform components while collaborating closely with Product, Engineering, Analytics, and Business teams.

What You'll Do

• Design, build, and optimize scalable batch and real-time data pipelines.
• Develop robust data models using DBT and modern data warehousing/lakehouse principles.
• Build production-grade reporting and analytics datasets.
• Own data quality, observability, monitoring, alerting, lineage, and documentation.
• Improve platform reliability, performance, and cost efficiency.
• Translate business and product requirements into scalable data solutions.
• Work with large-scale transactional and behavioral datasets.
• Build reusable data assets and self-serve analytics capabilities.
• Ensure data governance, privacy, compliance, and retention policies are implemented effectively.
• Collaborate with engineering teams to integrate data products into business applications.
• Contribute to architecture decisions and continuously improve the overall data platform.
• Leverage AI-assisted development tools to improve engineering productivity, testing, documentation, and operational efficiency.

What We're Looking For

Must Have

• 6–10 years of experience in Data Engineering or Analytics Engineering.
• Strong proficiency in Python and SQL.
• Hands-on experience with DBT and OLAP databases (ClickHouse preferred).
• Experience designing and operating large-scale data platforms, data warehouses, or lakehouses.
• Strong understanding of distributed systems, data modeling, and ETL/ELT architecture.
• Experience with data quality frameworks, testing, CI/CD, and observability practices.
• Ability to independently debug production issues and drive resolution.
• Strong communication skills and ability to work with cross-functional stakeholders.
• Understanding of data governance, privacy, and compliance requirements.

Preferred

• Experience with Apache Airflow, Spark, Flink, or Kafka.
• Exposure to Iceberg, Redshift, Pinot, Glue Jobs, or similar modern data platform technologies.
• Experience building streaming data pipelines and real-time analytics systems.
• Familiarity with MLOps, feature stores, or AI/ML data infrastructure.
• Prior experience in fintech, payments, or high-volume transaction systems.

What Success Looks Like

• Reliable, scalable, and well-documented data pipelines.
• High data quality and platform uptime.
• Faster access to business insights through self-serve analytics.
• Efficient collaboration between Data, Product, Engineering, and Business teams.
• Continuous improvement of platform performance, governance, and developer productivity.

Why Join Us

• Build data infrastructure that powers large-scale fintech products.
• Work on high-volume transactional and behavioral datasets.
• Influence architecture decisions and platform evolution.
• Be part of a fast-moving, ownership-driven engineering culture.
• Work with modern data technologies and AI-assisted engineering practices.

This role is ideal for engineers who enjoy owning data systems end-to-end, solving complex scalability challenges, and building platforms that directly impact business outcomes.